Shina Daniel Oloniiju, Yusuf Olatunji Tijani, Olumuyiwa Otegbeye
{"title":"Thermal analysis of extended surfaces using deep neural networks","authors":"Shina Daniel Oloniiju, Yusuf Olatunji Tijani, Olumuyiwa Otegbeye","doi":"10.1515/phys-2024-0051","DOIUrl":null,"url":null,"abstract":"The complexity of thermal analysis in practical systems has emerged as a subject of interest in various fields of science and engineering. Extended surfaces, commonly called fins, are crucial cooling and heating mechanisms in many applications, such as refrigerators and power plants. In this study, by using a deterministic approach, we discuss the thermal analysis of conduction, convection, and radiation in the presence of a magnetic force within an extended surface. The present study develops a deep neural network to analyze the mathematical model and to estimate the contributions of each dimensionless model parameter to the thermal dynamics of fins. The deep neural network used in this study makes use of a feedforward architecture in which the weights and biases are updated through backward propagation. The accuracy of the neural network model is validated using results obtained from a spectral-based linearization method. The efficiency rate of the extended surfaces is computed using the neural network and spectral methods. The results obtained demonstrate the accuracy of the neural network-based technique. The findings of this study in relation to the novel mathematical model reveal that utilizing materials with variable thermal conductivity enhances the efficiency rate of the extended surface.","PeriodicalId":48710,"journal":{"name":"Open Physics","volume":"25 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Open Physics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1515/phys-2024-0051","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
The complexity of thermal analysis in practical systems has emerged as a subject of interest in various fields of science and engineering. Extended surfaces, commonly called fins, are crucial cooling and heating mechanisms in many applications, such as refrigerators and power plants. In this study, by using a deterministic approach, we discuss the thermal analysis of conduction, convection, and radiation in the presence of a magnetic force within an extended surface. The present study develops a deep neural network to analyze the mathematical model and to estimate the contributions of each dimensionless model parameter to the thermal dynamics of fins. The deep neural network used in this study makes use of a feedforward architecture in which the weights and biases are updated through backward propagation. The accuracy of the neural network model is validated using results obtained from a spectral-based linearization method. The efficiency rate of the extended surfaces is computed using the neural network and spectral methods. The results obtained demonstrate the accuracy of the neural network-based technique. The findings of this study in relation to the novel mathematical model reveal that utilizing materials with variable thermal conductivity enhances the efficiency rate of the extended surface.
期刊介绍:
Open Physics is a peer-reviewed, open access, electronic journal devoted to the publication of fundamental research results in all fields of physics. The journal provides the readers with free, instant, and permanent access to all content worldwide; and the authors with extensive promotion of published articles, long-time preservation, language-correction services, no space constraints and immediate publication. Our standard policy requires each paper to be reviewed by at least two Referees and the peer-review process is single-blind.